Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI:10.14569/ijacsa.2023.0140808
Mohamed Elashmawy, I. Elamvazuthi, L. I. Izhar, S. Paramasivam, Steven W. Su
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Abstract

—The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90% and a 0.8975 F1-score with the proposed pre-processing method.
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基于混合预处理深度学习方法的肺结核检测
-结核病是一种严重的健康问题,因为它主要影响肺部并可导致死亡。然而,早期发现和治疗可以治愈这种疾病。检测结核病的一种潜在方法是使用计算机辅助诊断(CAD)系统,该系统可以分析胸部x射线图像(CXR)以寻找结核病的迹象。本文提出了一种利用卷积神经网络(CNN)模型的混合预处理方法来提高CAD系统性能的新方法。本研究的目的是通过结合两种不同的预处理方法,对结核病的不同表现进行多分类,提高CXR图像对结核病检测的准确性和曲线下面积(Area Under Curve, AUC)。假设这种方法将导致在CXR图像中更准确地检测结核病。为了实现这一点,本研究使用增强和分割技术对CXR图像进行预处理,然后将其输入预训练的CNN模型进行分类。采用该预处理方法,VGG16模型的AUC为0.935,准确率为90%,f1分数为0.8975。
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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